40 MHz Scouting with Deep Learning in CMS

A 40 MHz scouting system at CMS would provide fast and virtually unlimited statistics for detector diagnostics, alternative luminosity measurements and, in some cases, calibrations, and it has the potential to enable the study of otherwise inaccessible signatures, either too common to fit in the L1 accept budget, or with requirements which are orthogonal to ``mainstream'' physics, such as long-lived particles. Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw inputs. A series of studies on different aspects of LHC data processing have demonstrated the potential of deep learning for CERN applications. The usage of deep learning aims at improving physics performance and reducing execution time. This talk will present a deep learning approach to muon scouting in the Level-1 Trigger of the CMS detector. The idea is to utilise multilayered perceptrons to ``re-fit''' the Level-1 muon tracks, using fully reconstructed offline tracking parameters as the ground truth for neural network training. The network produces corrected helix parameters (transverse momentum, $\eta$ and $\phi$), with a precision that is greatly improved over the standard Level 1 reconstruction. The network is executed on an FPGA-based PCIe board produced by Micron Technology, the SB-852. It is implemented using the Micron Deep Learning Accelerator inference engine. The methodology for developing deep learning models will be presented, alongside the process of compiling the models for fast inference hardware. The metrics for evaluating performance and the achieved results will be discussed

The New CMS DAQ System for Run 2 of the LHC

The data acquisition system (DAQ) of the CMS experiment at the CERN Large Hadron Collider assembles events at a rate of 100 kHz, transporting event data at an aggregate throughput of 100 GB/s to the high level trigger (HLT) farm. The HLT farm selects interesting events for storage and offline analysis at a rate of around 1 kHz. The DAQ system has been redesigned during the accelerator shutdown in 2013/14. The motivation is twofold Firstly, the current compute nodes, networking, and storage infrastructure will have reached the end of their lifetime by the time the LHC restarts. Secondly, in order to handle higher LHC luminosities and event pileup, a number of sub-detectors will be upgraded, increasing the number of readout channels and replacing the off-detector readout electronics with a micro-TCA implementation. The new DAQ architecture will take advantage of the latest developments in the computing industry. For data concentration, 10/40 Gb/s Ethernet technologies will be used, as well as an implementation of a reduced TCP/IP in FPGA for a reliable transport between custom electronics and commercial computing hardware. A 56 Gb/s Infiniband FDR CLOS network has been chosen for the event builder with a throughput of ~4 Tbps. The HLT processing is entirely file based. This allows the DAQ and HLT systems to be independent, and to use the same framework for the HLT as for the offline processing. The fully built events are sent to the HLT with 1/10/40 Gb/s Ethernet via network file systems. Hierarchical collection of HLT accepted events and monitoring meta-data are stored into a global file system. This paper presents the requirements, technical choices, and performance of the new system

Achieving High Performance With TCP Over 40 GbE on NUMA Architectures for CMS Data Acquisition

TCP and the socket abstraction have barely changed over the last two decades, but at the network layer there has been a giant leap from a few megabits to 100 gigabits in bandwidth. At the same time, CPU architectures have evolved into the multi-core era and applications are expected to make full use of all available resources. Applications in the data acquisition domain based on the standard socket library running in a Non-Uniform Memory Access (NUMA) architecture are unable to reach full efficiency and scalability without the software being adequately aware about the IRQ (Interrupt Request), CPU and memory affinities. During the first long shutdown of LHC, the CMS DAQ system is going to be upgraded for operation from 2015 onwards and a new software component has been designed and developed in the CMS online framework for transferring data with sockets. This software attempts to wrap the low-level socket library to ease higher-level programming with an API based on an asynchronous event driven model similar to the DAT uDAPL API. It is an event-based application with NUMA optimizations, that allows for a high throughput of data across a large distributed system. This paper describes the architecture, the technologies involved and the performance measurements of the software in the context of the CMS distributed event building

The New CMS DAQ System for Run-2 of the LHC

Abstract—The data acquisition (DAQ) system of the CMS experiment at the CERN Large Hadron Collider assembles events at a rate of 100 kHz, transporting event data at an aggregate throughput of 100 GB/s to the high level trigger (HLT) farm. The HLT farm selects interesting events for storage and offline analysis at a rate of around 1 kHz. The DAQ system has been redesigned during the accelerator shutdown in 2013/14. The motivation is twofold: Firstly, the current compute nodes, networking, and storage infrastructure will have reached the end of their lifetime by the time the LHC restarts. Secondly, in order to handle higherLHC luminosities and event pileup, a number of sub-detectors will be upgraded, increasing the number of readout channelsand replacing the off-detector readout electronics with a TCA implementation. The new DAQ architecture will take advantageof the latest developments in the computing industry. For data concentration, 10/40 Gb/s Ethernet technologies will be used, aswell as an implementation of a reduced TCP/IP in FPGA for a reliable transport between custom electronics and commercialcomputing hardware. A Clos network based on 56 Gb/s FDR Infiniband has been chosen for the event builder with a throughputof Tb/s. The HLT processing is entirely file based. This allows the DAQ and HLT systems to be independent, and to use the HLTsoftware in the same way as for the offline processing. The fully built events are sent to the HLT with 1/10/40 Gb/s Ethernet vianetwork file systems. Hierarchical collection of HLT accepted events and monitoring meta-data are stored into a global filesystem. This paper presents the requirements, technical choices, and performance of the new system. Index Terms—Data acquisition, high energy physics

Opportunistic usage of the CMS online cluster using a cloud overlay

After two years of maintenance and upgrade, the Large Hadron Collider (LHC), the largest and most powerful particle accelerator in the world, has started its second three year run. Around 1500 computers make up the CMS (Compact Muon Solenoid) Online cluster. This cluster is used for Data Acquisition of the CMS experiment at CERN, selecting and sending to storage around 20 TBytes of data per day that are then analysed by the Worldwide LHC Computing Grid (WLCG) infrastructure that links hundreds of data centres worldwide. 3000 CMS physicists can access and process data, and are always seeking more computing power and data. The backbone of the CMS Online cluster is composed of 16000 cores which provide as much computing power as all CMS WLCG Tier1 sites (352K HEP-SPEC-06 score in the CMS cluster versus 300K across CMS Tier1 sites). The computing power available in the CMS cluster can significantly speed up the processing of data, so an effort has been made to allocate the resources of the CMS Online cluster to the grid when it isn’t used to its full capacity for data acquisition. This occurs during the maintenance periods when the LHC is non-operational, which corresponded to 117 days in 2015. During 2016, the aim is to increase the availability of the CMS Online cluster for data processing by making the cluster accessible during the time between two physics collisions while the LHC and beams are being prepared. This is usually the case for a few hours every day, which would vastly increase the computing power available for data processing. Work has already been undertaken to provide this functionality, as an OpenStack cloud layer has been deployed as a minimal overlay that leaves the primary role of the cluster untouched. This overlay also abstracts the different hardware and networks that the cluster is composed of. The operation of the cloud (starting and stopping the virtual machines) is another challenge that has been overcome as the cluster has only a few hours spare during the aforementioned beam preparation. By improving the virtual image deployment and integrating the OpenStack services with the core services of the Data Acquisition on the CMS Online cluster it is now possible to start a thousand virtual machines within 10 minutes and to turn them off within seconds. This document will explain the architectural choices that were made to reach a fully redundant and scalable cloud, with a minimal impact on the running cluster configuration while giving a maximal segregation between the services. It will also present how to cold start 1000 virtual machines 25 times faster, using tools commonly utilised in all data centres

Opportunistic usage of the CMS online cluster using a cloud overlay

After two years of maintenance and upgrade, the Large Hadron Collider (LHC), the largest and most powerful particle accelerator in the world, has started its second three year run. Around 1500 computers make up the CMS (Compact Muon Solenoid) Online cluster. This cluster is used for Data Acquisition of the CMS experiment at CERN, selecting and sending to storage around 20 TBytes of data per day that are then analysed by the Worldwide LHC Computing Grid (WLCG) infrastructure that links hundreds of data centres worldwide. 3000 CMS physicists can access and process data, and are always seeking more computing power and data. The backbone of the CMS Online cluster is composed of 16000 cores which provide as much computing power as all CMS WLCG Tier1 sites (352K HEP-SPEC-06 score in the CMS cluster versus 300K across CMS Tier1 sites). The computing power available in the CMS cluster can significantly speed up the processing of data, so an effort has been made to allocate the resources of the CMS Online cluster to the grid when it isn't used to its full capacity for data acquisition. This occurs during the maintenance periods when the LHC is non-operational, which corresponded to 117 days in 2015. During 2016, the aim is to increase the availability of the CMS Online cluster for data processing by making the cluster accessible during the time between two physics collisions while the LHC and beams are being prepared. This is usually the case for a few hours every day, which would vastly increase the computing power available for data processing. Work has already been undertaken to provide this functionality, as an OpenStack cloud layer has been deployed as a minimal overlay that leaves the primary role of the cluster untouched. This overlay also abstracts the different hardware and networks that the cluster is composed of. The operation of the cloud (starting and stopping the virtual machines) is another challenge that has been overcome as the cluster has only a few hours spare during the aforementioned beam preparation. By improving the virtual image deployment and integrating the OpenStack services with the core services of the Data Acquisition on the CMS Online cluster it is now possible to start a thousand virtual machines within 10 minutes and to turn them off within seconds. This document will explain the architectural choices that were made to reach a fully redundant and scalable cloud, with a minimal impact on the running cluster configuration while giving a maximal segregation between the services. It will also present how to cold start 1000 virtual machines 25 times faster, using tools commonly utilised in all data centres

Online data handling and storage at the CMS experiment

During the LHC Long Shutdown 1, the CMS Data Acquisition (DAQ) system underwent a partial redesign to replace obsolete network equipment, use more homogeneous switching technologies, and support new detector back-end electronics. The software and hardware infrastructure to provide input, execute the High Level Trigger (HLT) algorithms and deal with output data transport and storage has also been redesigned to be completely file- based. All the metadata needed for bookkeeping are stored in files as well, in the form of small 'documents' using the JSON encoding. The Storage and Transfer System (STS) is responsible for aggregating these files produced by the HLT, storing them temporarily and transferring them to the T0 facility at CERN for subsequent offline processing. The STS merger service aggregates the output files from the HLT from ~62 sources produced with an aggregate rate of ~2GB/s. An estimated bandwidth of 7GB/s in concurrent read/write mode is needed. Furthermore, the STS has to be able to store several days of continuous running, so an estimated of 250TB of total usable disk space is required. In this article we present the various technological and implementation choices of the three components of the STS: the distributed file system, the merger service and the transfer system

Boosting Event Building Performance using Infiniband FDR for CMS Upgrade

As part of the CMS upgrade during CERN long shutdown period (LS1), the CMS data acquisition system is incorporating Infiniband FDR technology to boost event building performance for operation from 2015 onwards. Infiniband promises to provide substantial increase in data transmission speeds compared to the older 1GE network used during the 2009-2013 LHC run. Several options exist to end user developers when choosing a foundation for software upgrades, including the uDAPL (DAT Collaborative) and Infiniband verbs libraries (OFED). Due to advances in technology, the CMS data acquisition system will be able to achieve the required throughput of 100 kHz with increased event sizes while downsizing the number of nodes by using a combination of 10GE, 40GE and 56 GB Infiniband FDR. This paper presents the analysis and results of a comparison between GE and Infiniband solutions as well as a look at how they integrate into an event building architecture, while preserving the scalability, efficiency and deterministic latency expected in a high end data acquisition network